CLASSIFICATION OF POLAR SATELLITE DATA USING IMAGE FEATURES AND DECISION TREE CLASSIFIER

In the polar region, it is difficult to discriminate between clouds and ground surface from satellite visible or infrared data, because of the high albedo and low surface temperature of snow and ice cover. In this paper, a method to classify clouds, sea ice and ground is proposed. This study is base...

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Bibliographic Details
Main Authors: ムラモト ケンイチロウ, ヤマノウチ タカシ, Kenichiro MURAMOTO, Takashi YAMANOUCHI
Format: Report
Language:English
Published: Department of Electrical and Computer Engineering, Faculty of Engineering, Kanazawa University 1996
Subjects:
Antarc*
Antarctica
Polar meteorology and glaciology
Proceedings of the NIPR Symposium on Polar Meteorology and Glaciology
Sea ice
Online Access:https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=3932
http://id.nii.ac.jp/1291/00003932/
https://nipr.repo.nii.ac.jp/?action=repository_action_common_download&item_id=3932&item_no=1&attribute_id=18&file_no=1
Description
Summary:In the polar region, it is difficult to discriminate between clouds and ground surface from satellite visible or infrared data, because of the high albedo and low surface temperature of snow and ice cover. In this paper, a method to classify clouds, sea ice and ground is proposed. This study is based upon analysis of the NOAA/AVHRR infrared images in Antarctica. The algorithm consists of two major approaches : estimating image features and a classification algorithm. A decision tree classifier is designed to classify the region into one of three classes using six image features. Though sea ice and ground can be largely separated using only one feature, more than three features are necessary to separate clouds.

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